{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import gamma, norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_0 = 200\n",
    "zeta_0 = 1\n",
    "alpha_0 = 1\n",
    "beta_0 = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_lambda_dist(alpha, beta):\n",
    "    return gamma(alpha, scale=1/beta)\n",
    "\n",
    "def gen_mu_dist(mu_mean, zeta, lmd):\n",
    "    return norm(loc=mu_mean, scale=np.sqrt(1/(zeta*lmd)))\n",
    "\n",
    "def draw(pdf, range_min, range_max, step):\n",
    "    xs = np.arange(range_min, range_max, step)\n",
    "    ys = [pdf.pdf(x) for x in xs]\n",
    "    plt.plot(xs, ys)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_dist = gen_lambda_dist(alpha_0, beta_0)\n",
    "draw(lambda_dist, 0, 5, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lmd = lambda_dist.rvs()\n",
    "mu_dist = gen_mu_dist(mu_0, zeta_0, lmd)\n",
    "draw(mu_dist, 180, 220, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "data  = pd.read_csv(\"sensor_data_200.txt\", delimiter=\" \", \n",
    "                    header=None, names = (\"date\",\"time\",\"ir\",\"lidar\"))\n",
    "lidar = data[\"lidar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[214 211 199 ... 204 207 208]\n",
      "平均:  209.73713297619855\n",
      "標準偏差:  4.838192492920729\n"
     ]
    }
   ],
   "source": [
    "samples = lidar   #.sample(5) #値を全部使う \n",
    "print(samples.values)\n",
    "print(\"平均: \", samples.mean())\n",
    "print(\"標準偏差: \", samples.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "209.73680284794034 58989 29495.0 692478.563742876\n"
     ]
    }
   ],
   "source": [
    "N = len(samples)\n",
    "mu_N = 1.0/(N+beta_0)*sum(samples) + beta_0/(N+beta_0)*mu_0\n",
    "zeta_N = N + zeta_0\n",
    "alpha_N = N/2 + alpha_0\n",
    "beta_N = 0.5*(sum([z**2 for z in samples]) + zeta_0*(mu_0**2) - zeta_N*(mu_N**2)) + beta_0\n",
    "print(mu_N, zeta_N, alpha_N, beta_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_dist = gen_lambda_dist(alpha_N, beta_N)  ###gaussgamma2postlambda\n",
    "draw(lambda_dist, 0, 5, 0.01)\n",
    "draw(lambda_dist, 0.035, 0.05, 0.0001) #範囲・縮尺を変えたもの"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均:  209.73680284794034\n",
      "標準偏差:  4.840413970905981\n"
     ]
    }
   ],
   "source": [
    "lmd = lambda_dist.rvs()   ###gaussgamma2postmu\n",
    "mu_dist = gen_mu_dist(mu_N, zeta_N, lmd)\n",
    "draw(mu_dist, 180, 220, 0.1)\n",
    "\n",
    "print(\"平均: \", mu_dist.mean())\n",
    "print(\"標準偏差: \", np.sqrt(1/lmd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt ###gaussgamma2compare\n",
    "\n",
    "z_dist = norm(loc=mu_dist.mean(), scale=np.sqrt(1/lmd))\n",
    "lidar.hist(bins = max(lidar) - min(lidar),align='left', color='orange')\n",
    "\n",
    "xs = np.arange(190, 230, 0.1)\n",
    "ys = [z_dist.pdf(x)*len(lidar) for x in xs]\n",
    "plt.plot(xs, ys)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "zs = np.arange(190,230, 0.1)\n",
    "ys = [norm.pdf(z, mu_dist.mean(), np.sqrt(1/lmd)) for z in zs]\n",
    "\n",
    "plt.plot(zs,ys)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
